

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__snake_case				:      Optional[int]   		=			TypeVar("""T""")


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			return (position - 1) // 2


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			return (2 * position) + 1


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			return (2 * position) + 2



class        A__(Generic[T]							):
						"""simple docstring"""






						def __init__(    self    )     -> None:
									a_						:						str 							= []
									a_						:						Optional[Any] 							= {}
									a_						:						Tuple 							= 0





						def __len__(    self    )     -> int:
									return self.elements





						def __repr__(    self    )     -> str:
									return str(self.heap    )





						def 			UpperCamelCase__					(    self    )     -> bool:
									return self.elements == 0





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase    )     -> None:
									self.heap.append((elem, weight)    )
									a_						:						Optional[Any] 							= self.elements
									self.elements += 1
									self._bubble_up(lowerCAmelCase__    )





						def 			UpperCamelCase__					(    self    )     -> T:


									if self.elements > 1:
												self._swap_nodes(0  ,      self.elements - 1    )
									a_							,					a_						:						Tuple 							= self.heap.pop()
									del self.position_map[elem]
									self.elements -= 1
									if self.elements > 0:
												a_							,					a_						:						Dict 							= self.heap[0]
												self._bubble_down(lowerCAmelCase__    )
									return elem





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase    )     -> None:
									a_						:						Dict 							= self.position_map[elem]
									a_						:						str 							= (elem, weight)
									if position > 0:
												a_						:						Any 							= get_parent_position(lowerCAmelCase__    )
												a_							,					a_						:						Optional[int] 							= self.heap[parent_position]
												if parent_weight > weight:
															self._bubble_up(lowerCAmelCase__    )
												else:
															self._bubble_down(lowerCAmelCase__    )
									else:
												self._bubble_down(lowerCAmelCase__    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> None:
									a_						:						str 							= self.position_map[elem]
									if curr_pos == 0:
												return None
									a_						:						Optional[int] 							= get_parent_position(lowerCAmelCase__    )
									a_							,					a_						:						int 							= self.heap[curr_pos]
									a_							,					a_						:						Optional[Any] 							= self.heap[parent_position]
									if parent_weight > weight:
												self._swap_nodes(lowerCAmelCase__  ,      lowerCAmelCase__    )
												return self._bubble_up(lowerCAmelCase__    )
									return None





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> None:
									a_						:						str 							= self.position_map[elem]
									a_							,					a_						:						Optional[int] 							= self.heap[curr_pos]
									a_						:						Optional[int] 							= get_child_left_position(lowerCAmelCase__    )
									a_						:						List[Any] 							= get_child_right_position(lowerCAmelCase__    )
									if child_left_position < self.elements and child_right_position < self.elements:
												a_							,					a_						:						int 							= self.heap[child_left_position]
												a_							,					a_						:						str 							= self.heap[child_right_position]
												if child_right_weight < child_left_weight and child_right_weight < weight:
															self._swap_nodes(lowerCAmelCase__  ,      lowerCAmelCase__    )
															return self._bubble_down(lowerCAmelCase__    )
									if child_left_position < self.elements:
												a_							,					a_						:						int 							= self.heap[child_left_position]
												if child_left_weight < weight:
															self._swap_nodes(lowerCAmelCase__  ,      lowerCAmelCase__    )
															return self._bubble_down(lowerCAmelCase__    )
									else:
												return None
									if child_right_position < self.elements:
												a_							,					a_						:						int 							= self.heap[child_right_position]
												if child_right_weight < weight:
															self._swap_nodes(lowerCAmelCase__  ,      lowerCAmelCase__    )
															return self._bubble_down(lowerCAmelCase__    )
									return None





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase    )     -> None:
									a_						:						List[Any] 							= self.heap[nodea_pos][0]
									a_						:						int 							= self.heap[nodea_pos][0]
									a_							,					a_						:						Optional[int] 							= (
									    self.heap[nodea_pos],
									    self.heap[nodea_pos],
									)
									a_						:						List[str] 							= nodea_pos
									a_						:						Union[str, Any] 							= nodea_pos







class        A__(Generic[T]							):
						"""simple docstring"""






						def __init__(    self    )     -> None:
									a_						:						Optional[int] 							= {}
									a_						:						Union[str, Any] 							= 0





						def __repr__(    self    )     -> str:
									return str(self.connections    )





						def __len__(    self    )     -> int:
									return self.nodes





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> None:


									if node not in self.connections:
												a_						:						List[Any] 							= {}
												self.nodes += 1





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase    )     -> None:
									self.add_node(lowerCAmelCase__    )
									self.add_node(lowerCAmelCase__    )
									a_						:						Union[str, Any] 							= weight
									a_						:						Dict 							= weight







def 					_UpperCAmelCase				(						a__  ,	):

			'''simple docstring'''



			a_						:						Any 							= {node: maxsize for node in graph.connections}
			a_						:						List[str] 							= {node: None for node in graph.connections}

			a_						:						Any 							= MinPriorityQueue()
			for node, weight in dist.items():
						priority_queue.push(a__  ,	a__)

			if priority_queue.is_empty():
						return dist, parent

			# initialization
			a_						:						str 							= priority_queue.extract_min()
			a_						:						Any 							= 0
			for neighbour in graph.connections[node]:
						if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
									a_						:						Tuple 							= dist[node] + graph.connections[node][neighbour]
									priority_queue.update_key(a__  ,	dist[neighbour])
									a_						:						Tuple 							= node

    # running prim's algorithm
			while not priority_queue.is_empty():
						a_						:						List[str] 							= priority_queue.extract_min()
						for neighbour in graph.connections[node]:
									if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
												a_						:						List[Any] 							= dist[node] + graph.connections[node][neighbour]
												priority_queue.update_key(a__  ,	dist[neighbour])
												a_						:						Dict 							= node
			return dist, parent



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					UNetaDModel
			__a    :					ScoreSdeVeScheduler





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							self.register_modules(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 20_00 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  **lowerCAmelCase__ ,  )	->   Union[ImagePipelineOutput, Tuple]:

							'''simple docstring'''


							__lowercase					     =				self.unet.config.sample_size
							__lowercase					     =				(batch_size, 3, img_size, img_size)

							__lowercase					     =				self.unet

							__lowercase					     =				randn_tensor(lowerCAmelCase__ ,  generator=lowerCAmelCase__		) * self.scheduler.init_noise_sigma
							__lowercase					     =				sample.to(self.device		)

							self.scheduler.set_timesteps(lowerCAmelCase__		)
							self.scheduler.set_sigmas(lowerCAmelCase__		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											__lowercase					     =				self.scheduler.sigmas[i] * torch.ones(shape[0] ,  device=self.device		)

											# correction step
											for _ in range(self.scheduler.config.correct_steps		):
															__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
															__lowercase					     =				self.scheduler.step_correct(lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# prediction step
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
											__lowercase					     =				self.scheduler.step_pred(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		)

											__lowercase     ,	__lowercase					     =				output.prev_sample, output.prev_sample_mean

							__lowercase					     =				sample_mean.clamp(0 ,  1		)
							__lowercase					     =				sample.cpu().permute(0 ,  2 ,  3 ,  1		).numpy()
							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return (sample,)

							return ImagePipelineOutput(images=lowerCAmelCase__		)